Non-Functional Requirements Elicitation Guideline for Agile Methods

One of the essential activities in software development is elicitation of requirement. Majority of the studies has pointed out that less attention is given to the NonFunctional Requirement (NFR). The negligence of NFR elicitation is due to lack of knowledge of the user and developer about NFR. Our study presents elicitation guidelines for NFRs in agile methods. This guideline will helps developers as well as users in agile methods. A case study is conducted on the group of master students for eliciting NFR with the help of elicitation guidelines. In addition, the initial results were obtained by extracting NFRs from eProcurement document that carries requirements of major European Union projects. The result of the case study is positive and encouraging for the new developers and users having less awareness about NFRs. Furthermore, the study describes the role of cloud computing in agile methods, especially in elicitation activity

On self-coordinating web services using similarity and neural networks

This paper presents an approach, which adopts control tuples, to support the self-coordination of Web services. Usually coordination is a designer-driven activity where, for instance, the designer clearly indicates the actions that Web services have to perform during conflict resolution. In this paper, Web services are embodied with the mechanisms, which allow them to coordinate themselves during run-time. These mechanisms are encoded using control tuples that Web services post on tuple spaces. Web services consult a tuple space and consume the control tuples, which are relevant to their coordination work. © 2005 IEEE

A Priority-Based Admission Control Scheme for Commercial Web Servers

This paper investigates into the performance and load management of web servers that are deployed in commercial websites. Such websites offer various services such as flight/hotel booking, online banking, stock trading, and product purchases among others. Customers are increasingly relying on these round-the-clock services which are easier and (generally) cheaper to order. However, such an increasing number of customers’ requests makes a greater demand on the web servers. This leads to web servers’ overload and the consequential provisioning of inadequate level of service. This paper addresses these issues and proposes an admission control scheme which is based on the class-based priority scheme that classifies customer’s requests into different classes. The proposed scheme is formally specified using ΠΠ-calculus and is implemented as a Java-based prototype system. The prototype system is used to simulate the behaviour of commercial website servers and to evaluate their performance in terms of response time, throughput, arrival rate, and the percentage of dropped requests. Experimental results demonstrate that the proposed scheme significantly improves the performance of high priority requests but without causing adverse effects on low priority requests

Securing Low-Power Blockchain-Enabled IoT Devices Against Energy Depletion Attack

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium. and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drain the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently

A tensor-based distributed discovery of missing association rules on the cloud

An increasing number of data applications such as monitoring weather data, data streaming, data web logs, and cloud data, are going online and are playing vital in our every-day life. The underlying data of such applications change very frequently, especially in the cloud environment. Many interesting events can be detected by discovering such data from different distributed sources and analyzing it for specific purposes (e.g., car accident detection or market analysis). However, several isolated events could be erroneous due to the fact that important data sets are either discarded or improperly analyzed as they contain missing data. Such events therefore need to be monitored globally and be detected jointly in order to understand their patterns and correlated relationships. In the context of current cloud computing infrastructure, no solutions exist for enabling the correlations between multi-source events in the presence of missing data. This paper addresses the problem of capturing the underlying latent structure of the data with missing entries based on association rules. This necessitate to factorize the data set with missing data. The paper proposes a novel model to handle high amount of data in cloud environment. It is a model of aggregated data that are confidences of association rules. We first propose a method to discover the association rules locally on each node of a cloud in the presence of missing rules. Afterward, we provide a tensor based model to perform a global correlation between all the local models of each node of the network. The proposed approach based on tensor decomposition, deals with a multi modal network where missing association rules are detected and their confidences are approximated. The approach is scalable in terms of factorizing multi-way arrays (i.e. tensor) in the presence of missing association rules. It is validated through experimental results which show its significance and viability in terms of detecting missing rules

Priority scheduling service for E-commerce web servers

Service scheduling is one of the crucial issues in E-commerce environment. E-commerce web servers often get overloaded as they have to deal with a large number of customers’ requests—for example, browse, search, and pay, in order to make purchases or to get product information from E-commerce web sites. In this paper, we propose a new approach in order to effectively handle high traffic load and to improve web server’s performance. Our solution is to exploit networking techniques and to classify customers’ requests into different classes such that some requests are prioritised over others. We contend that such classification is financially beneficial to E-commerce services as in these services some requests are more valuable than others. For instance, the processing of “browse” request should get less priority than “payment” request as the latter is considered to be more valuable to the service provider. Our approach analyses the arrival process of distinct requests and employs a priority scheduling service at the network nodes that gives preferential treatment to high priority requests. The proposed approach is tested through various experiments which show significant decrease in the response time of high priority requests. This also reduces the probability of dropping high priority requests by a web server and thus enabling service providers to generate more revenue

MILK PRODUCTION POTENTIAL OF PURE BRED HOLSTEIN FRIESIAN AND JERSEY COWS IN SUBTROPICAL ENVIRONMENT OF PAKISTAN

The data on 575 records of 270 Holstein Friesian and 818 records of 326 Jersey cows maintained in Punjab, Pakistan were analyzed. The cows were grouped into imported Holstein Friesian, imported Jersey, Farm born Holstein Friesian and farm born Jersey cows. Lactation milk yield of farm born Holstein Friesian and Jersey cows was significantly (P<0.05) lower than that of imported Holstein Friesian and Jersey cows. Breed group, season of calving and lactation number had significant (P<0.05) effect on lactation milk yield. The highest lactation milk yield was observed in imported and farm born Holstein Friesian cows calved during autumn, while in imported Jersey cows maximum lactation milk yield was observed in cows calved during spring season. The maximum lactation milk yield was observed in the third lactation in imported Holstein Friesian, imported Jersey and farm born Holstein Friesian cows, while in farm born Jersey cows maximum lactation milk yield was observed in the fifth lactation. The milk yield in all breed groups increased with increase in lactation length and service period

A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations

Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information

Searches for lepton-flavour-violating decays of the Higgs boson into eτ and μτ in \sqrt{s} = 13 TeV pp collisions with the ATLAS detector

Abstract This paper presents direct searches for lepton flavour violation in Higgs boson decays, H → eτ and H → μτ, performed using data collected with the ATLAS detector at the LHC. The searches are based on a data sample of proton-proton collisions at a centre-of-mass energy s $$\sqrt{s}$$ = 13 TeV, corresponding to an integrated luminosity of 138 fb−1. Leptonic (τ → ℓνℓντ) and hadronic (τ → hadrons ντ) decays of the τ-lepton are considered. Two background estimation techniques are employed: the MC-template method, based on data-corrected simulation samples, and the Symmetry method, based on exploiting the symmetry between electrons and muons in the Standard Model backgrounds. No significant excess of events is observed and the results are interpreted as upper limits on lepton-flavour-violating branching ratios of the Higgs boson. The observed (expected) upper limits set on the branching ratios at 95% confidence level, B $$\mathcal{B}$$ (H → eτ) < 0.20% (0.12%) and B $$\mathcal{B}$$ (H → μτ ) < 0.18% (0.09%), are obtained with the MC-template method from a simultaneous measurement of potential H → eτ and H → μτ signals. The best-fit branching ratio difference, B $$\mathcal{B}$$ (H → μτ) → B $$\mathcal{B}$$ (H → eτ), measured with the Symmetry method in the channel where the τ-lepton decays to leptons, is (0.25 ± 0.10)%, compatible with a value of zero within 2.5σ